1. Field of the Invention
The present invention relates to electronic computerized medical instruments and more particularly to the localization and characterization of the generators of brain and heart electric and magnetic activity by a non-invasive computerized method and system.
2. Description of Related Art
The determination of the three dimensional localization and of the temporal activity of the neuronal generators which give place to waveshapes, in an electroencephalogram (EEG) and an magnetoencephalogram (MEG) related to pathologies of the central nervous system (CNS), constitutes an important medical problem. Such knowledge can be helpful in producing more precise diagnostics in diverse neuropsychiatric pathologies and in determining more efficient treatments. A typical example is the study of the focus location followed by its sequential propagation in epilepsies that are being evaluated for surgical treatment.
The EEG and the MEG both have their common origin in the ionic currents produced by the cellular elements (the neurons) composing the CNS. The total current density vector field is determined by the vectorial additive combination of all of the elementary currents. The simultaneous activation of a large number of such elements, together with an adequate geometrical distribution, produces resulting electric potentials and magnetic fields which can be measured outside the head. In the transformation process from total current density to measurable external fields, the effects of the volume conductor properties of the different tissues composing the head must be taken into account: brain, meninges, cerebral spinal fluid, skull, and scalp.
The resulting measured fields have the characteristics of a stochastic process, which can be described either in the frequency domain or in the temporal domain, as a function of the statistical moments. In the case of a Gaussian process, first and second order moments give an exhaustive description.
The neural elements which generate a given EEG or MEG component may be localized on a small cortical area ("concentrated generator") or may, on the other hand, be widely distributed in different parts of the CNS ("diffuse generator"). The determination of the spatial distribution of the generators and of the multivariate statistical moments describing their interactions is very important.
For a number of decades electric potential measurements of the CNS have been performed by means of electrodes placed on the scalp. Much experience has accumulated on the practical utility of the visual inspection of the EEG in the diagnostics and treatment of patients with neuropsychiatric diseases. More recently, brain magnetic fields have been measured (U.S. Pat. No. 4,591,787), offering complementary information to that obtained from the EEG.
The current state of the art, as reflected in U.S. Pat. Nos. 4,201,224; 4,408,616; and 4,417,592 is summarized as follows. Quantitative analysis of brain electric activity by means of digital signal processing methods (QEEG) allows an objective evaluation of the functioning of the CNS. The signal recorded at each electrode is summarized by means of a set of descriptive parameters (DPs), based on stochastic process modeling. The DPs reflect the normal and pathological functioning of the CNS. Topographic maps based on the DPs are clinically useful, and even more so when statistically compared to a normative data base.
However, this analysis method generates an excessively large number of DPs, thus making quite difficult the evaluation of a particular patient. Moreover, the method does not attempt to localize the generators responsible for the measured DPs, thus limiting the clinical usefulness and contributing to the excessive redundancy of the DPs due to volume conduction effects. Finally, EEG is limited to the study of second order moments in the frequency domain, which means that the EEG has been implicitly assumed to be a Gaussian stochastic process, despite evidence revealing the non-linear nature of such signals.
In U.S. Pat. No. 4,913,160 a method for the reduction of the dimensionality of the DPs is proposed based on principal components (PCs) computation. This procedure produces minimum sets of linear combinations of the original DPs, with optimum descriptive properties, but which are meaningless in terms of the underlying neuronal generators and their localization. Furthermore, this method does not take into account the non-linear nature of the original signals.
An improvement in the usefulness of QEEG has been achieved by means of biophysical models which take into account the behavior of the electromagnetic fields produced by current sources in a complex volume conductor such as the human head. In this sense, U.S. Pat. Nos. 4,416,288; 4,753,246; and 4,736,751 propose procedures for eliminating the distortion effects due to the volume conductor. However, they do not deal with the spatial characterization of the generators.
Several attempts have been made to fit equivalent dipoles to measured fields in order to represent, albeit approximately, concentrated generators, either in the time domain or in the frequency domain. These procedures are based on the minimization of a certain distance criterion between the measurements and the theoretical field values due to a current dipole inside a volume conductor model of the head.
This type of procedure for source localization, based on first order moment data, does not take into account the existence of diffuse generators, nor the existence of other sources of "spatial noise". Furthermore, a statistical method for testing the goodness of fit of the source model is not provided. On the other hand, there is a fundamental limit on the number of dipoles that can be estimated, the maximum number being roughly equal to the number of electric or magnetic signals divided by six.
In French patent 2,622,990, several improvements are achieved by using frequency domain second order moment data, in the form of coherence matrices. An estimation method for the cross spectral spatial noise matrix is proposed, under the assumption of interelectrode independence, the method thus being statistically equivalent to the classical factor analysis model. The eigenvectors of the common factor space are then used for determining the concentrated generators (as many as the number of common factors).
However, empirical and theoretical evidence points towards a diffuse generator model for spatial noise, producing a structured cross spectral noise matrix for EEG and MEG. This explains why the proposed noise elimination method under the interelectrode independence assumption gives incorrect results. In such a case computations based on coherence matrices are not justified. Furthermore, dipole fitting methods applied to second order moment data or to eigenvector data are not equivalent. Finally, interactions between generators, neither linear nor non-linear, are taken into account in the eigenvector dipole fitting approach.